Personalized Recommendation Computation in Real Time using Incremental Matrix Factorization and User Factor Clustering

Abstract

Recommendation control techniques using incremental matrix factorization and clustering are described. User latent factors and item latent factors are computed from data that denotes ratings associated with the users regarding respective ones of the plurality of items of digital content. Data is obtained that describes interaction of a particular one of the users with at least one respective item of the digital content. A plurality of clusters is formed using the user latent factors. The recommendations are generated using the user latent factors and the item latent factors for each of the plurality of clusters. Further, at least one of recommendations is located based on comparison of a user identifier of a subsequent user with the plurality of clusters. Interaction of the subsequent user with the digital content is controlled based on the located at least one of the recommendations.

What is claimed is: 
     
         1 . In a digital medium environment to generate recommendations to control user interaction with digital content, a method implemented by at least one computing device comprising:
 computing user latent factors and item latent factors by the at least one computing device from data that denotes ratings associated with the users regarding respective ones of the plurality of items of digital content;   obtaining data by the at least one computing device that describes interaction of a particular one of the users with at least one respective item of the digital content;   updating the user latent factor that corresponds to the particular one of the users using the obtained data by the at least one computing device; and   generating at least one of the recommendations by the computing device using the updated user latent factors, the recommendation configured to control subsequent interaction of the particular user with digital content of the service provider.   
     
     
         2 . The method as described in  claim 1 , wherein the user latent factors are defined using a user latent factor matrix, the item latent factors are defined using an item latent factor matrix, and the data that denotes ratings associated with the users regarding respective ones of the plurality of items is defined by a user-item matrix. 
     
     
         3 . The method as described in  claim 2 , wherein the updating is performed solely for the user latent factor that corresponds to the particular one of the users and not other parts of the user latent factor matrix. 
     
     
         4 . The method as described in  claim 2 , wherein the user latent factor matrix and the item latent factor matrix are calculated using a matrix factorization technique from the user-item matrix. 
     
     
         5 . The method as described in  claim 4 , wherein the matrix factorization technique is performed using a plurality of iterations is which one of the user latent factor matrix or the item latent factor matrix is kept fixed while the other one of the user latent factor matrix or the item latent factor matrix is recomputed until convergence. 
     
     
         6 . The method as described in  claim 4 , wherein the matrix factorization technique includes an alternating least squares technique. 
     
     
         7 . The method as described in  claim 1 , wherein the rating associated with the user regarding the respective ones of the plurality of items is obtained explicitly from the user for the items or is derived implicitly based on how each of the users interacts with the respective ones of the plurality of items. 
     
     
         8 . The method as described in  claim 1 , further comprising repeating the computing beginning with the user latent factors and the item latent factors to form subsequent user latent factors and item latent factors. 
     
     
         9 . The method as described in  claim 1 , further comprising clustering the user latent factors into a plurality of clusters, generating recommendations for each of the plurality of clusters, receiving a user identifier of the subsequent user, determining which of the plurality of clusters correspond to the subsequent user based on the user identifier, and locating at least one of the generated recommendations to control interaction of the subsequent user with the digital content of the service provider. 
     
     
         10 . In a digital medium environment to generate recommendations to control user interaction with digital content, a method implemented by at least one computing device comprising:
 computing user latent factors and item latent factors by the at least one computing device from data that denotes ratings associated with the users regarding respective ones of the plurality of items of digital content;   forming a plurality of clusters using the user latent factors by the at least one computing device; and   generating the recommendations by the at least one computing device using the user latent factors and the item latent factors for each of the plurality of clusters, the recommendations located based on correspondence of subsequent users with respective ones of the clusters to locate corresponding recommendations to control subsequent interaction of the users with digital content of the service provider.   
     
     
         11 . The method as described in  claim 10 , wherein the clustering is performed by the at least one computing device using a K-means clustering technique. 
     
     
         12 . The method as described in  claim 10 , further comprising:
 obtaining data by the at least one computing device that describes interaction of a particular one of the users with at least one respective item of the digital content; and   updating the user latent factor that corresponds to the particular one of the users using the obtained data by the at least one computing device.   
     
     
         13 . The method as described in  claim 10 , wherein the user latent factors are defined using a user latent factor matrix, the item latent factors are defined using an item latent factor matrix, and the data that denotes ratings associated with the users regarding respective ones of the plurality of items is defined by a user-item matrix. 
     
     
         14 . The method as described in  claim 13 , wherein the user latent factor matrix and the item latent factor matrix are calculated using a matrix factorization technique from the user-item matrix. 
     
     
         15 . The method as described in  claim 14 , wherein the matrix factorization technique is performed using a plurality of iterations is which one of the user latent factor matrix or the item latent factor matrix is kept fixed while the other one of the user latent factor matrix or the item latent factor matrix is recomputed until convergence. 
     
     
         16 . The method as described in  claim 14 , wherein the matrix factorization technique includes an alternating least squares technique. 
     
     
         17 . In a digital medium environment to control user interaction with digital content based on recommendations, a system implemented by at least one computing device to perform operations comprising:
 computing user latent factors and item latent factors from data that denotes ratings associated with the users regarding respective ones of the plurality of items of digital content;   obtaining data that describes interaction of a particular one of the users with at least one respective item of the digital content;   updating the user latent factor that corresponds to the particular one of the users using the obtained data;   forming a plurality of clusters using the user latent factors;   generating the recommendations using the user latent factors and the item latent factors for each of the plurality of clusters;   locating at least one of recommendations based on comparison of a user identifier of a subsequent user with the plurality of clusters; and   controlling interaction of the subsequent user with the digital content based on the located at least one of the recommendations.   
     
     
         18 . The system as described in  claim 17 , wherein the forming is performed such that similar users are included in a same said cluster. 
     
     
         19 . The system as described in  claim 17 , wherein the generating includes precomputing the recommendations for a centroid of each said cluster such that a number of the recommendations precomputed for each said cluster is greater than a number of the recommendations used as part of the locating. 
     
     
         20 . The system as described in  claim 19 , wherein the locating is performed based at least in part on a dot product of a user latent factor of the subsequent user and the item latent factors for items in the precomputed set of recommendations for a corresponding said cluster.